Edge-Enabled RPA Workflow Execution Without Central Server Delays
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Solution Overview
Problem
In Robotic Process Automation (RPA), the dependency of RPA bots on a centralized server leads to communication delays, which can cause failures in business-critical operations.
Innovation Solution
A decentralized edge computing system is implemented, where a hosted virtual desktop (HVD) receives instructions from a controller hosted virtual desktop (CHVD), executes tasks independently, and uses an edge computing enablement engine to predict and manage subsequent tasks, reducing dependency on the central server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized RPA server coordinates multiple RPA bots, then task allocation and management are centralized, but communication delays occur and business critical operations fail
Solution Approach 1:
The patent segments the centralized RPA architecture into decentralized edge computing nodes. Each RPA bot is equipped with local edge computing capabilities that enable autonomous task execution without continuous centralized server coordination. This segmentation eliminates communication delays by allowing bots to operate independently while maintaining task allocation through the centralized server only when necessary.
Solution Approach 2:
The patent implements preliminary action by pre-loading task instructions and parameters into the edge computing enablement engine before execution. The system prepares execution environments, loads necessary software components, and configures runtime parameters in advance, allowing RPA bots to immediately execute tasks without real-time server communication delays.
2Productivity
If RPA bots depend on centralized server for task execution, then centralized control is maintained, but execution speed decreases due to communication overhead
Solution Approach 1:
The patent enables self-service by equipping RPA bots with edge computing enablement engines that autonomously manage task execution. The bots independently load instructions, execute tasks, monitor their own performance, and manage local resources without continuous centralized server intervention. This self-service capability dramatically increases execution speed by eliminating communication overhead while the modular edge computing architecture manages complexity through standardized interfaces.
3Loss of time
If decentralized edge computing is implemented, then communication delays are reduced, but system architecture complexity increases
Solution Approach 1:
The patent applies universality by designing a multi-functional edge computing enablement engine that consolidates multiple capabilities into a single modular component. This engine handles task instruction loading, execution environment preparation, software component management, runtime parameter configuration, and performance monitoring. By universalizing these functions in a standardized module, the system reduces communication delays through decentralization while managing architecture complexity through reusability and standardization.
Solution Approach 2:
The patent introduces an intermediary layer in the form of the edge computing enablement engine that mediates between the centralized RPA server and the RPA bot execution environment. This intermediary pre-processes task instructions, prepares execution environments, and buffers communication, reducing the frequency and complexity of interactions between the centralized server and distributed bots, thereby managing system architecture complexity.
Data Source
AI summary
Systems, computer program products, and methods are described herein for decentralized edge computing enablement in robotic process automation. The present invention is configured to receive an indication that a hosted virtual desktop (HVD) has received a first set of instructions for execution from a controller hosted virtual desktop (CHVD); electronically receive, from the HVD, an indication that the first set of instructions have been executed by the HVD; predict, using the edge computing enablement engine, a second task to be executed by the HVD; determine, using the quantum database search algorithm, a location of the second task in the knowledge repository; retrieve a second set of instructions associated with the second task from the location of the second task in the knowledge repository; and receive, from the HVD, an indication that the second set of instructions have been executed by the HVD.